Order of experimentation for metamodeling tasks.

The order in which measurements are carried out, determines the accuracy of models in early stages of the measurement process, i.e. while measurements are still in progress. Reliable models in early stages of the data acquisition phase allow for model-based investigations like optimization runs or an earlier switching to an active learning phase. This paper compares different methods to determine the order of experimentation for regression problems in metamodeling tasks. The data distribution and the data density in the input space are varied for several randomly generated synthetic functions in order to find the most promising determination strategy for the order of experimentation. As an application example, all strategies are also applied to a computational fluid dynamics (CFD) metamodel. The order of experimentation based on the intelligent k-means clustering algorithm turns out to be the best overall order-determination strategy.